(1) Field of Invention
The present invention relates to three-dimensional imaging and, more particularly, to a method and device for high-resolution three-dimensional imaging which obtains camera pose using defocusing.
(2) Description of Related Art
The accurate determination of a moving camera position is critical when reconstructing three-dimensional (3-D) images of an object. If the 3-D locations of at least three points on an object are known at two different time instances, one can determine the camera coordinate transformation between the two time instances by analyzing the known coordinates with the Levenberg-Marquardt minimization method, as disclosed in [3] and [4]. The problem with current 3-D imaging methods resides in the fact that the 3-D position of object features are usually obtained via inaccurate techniques which ultimately limit the accuracy and resolution of the 3-D reconstruction of the object. Current methods in computer vision use either mono or stereo features to find camera pose, or the “structure from motion” techniques described in [1]. The main drawback of these techniques is that the resolution is limited to approximately 200 microns. This resolution range is insufficient to support many practical applications, such as dental imaging, which requires a resolution of approximately 25-50 microns. Also, the current techniques can produce large error levels when imaging an object which does not have many detectable corners.
Thus, a continuing need exists for a method and device for 3-D imaging which can resolve camera pose and produce a high-resolution 3-D image of the object.
(3) References Cited
[1] F. Dellaert, S. Seitz, C. Thorpe, and S. Thrun (2000), “Structure from motion without correspondence,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 
[2] C. Willert and M. Gharib (1992), “Three-dimensional particle imaging with a single camera,” Experiments in Fluids 12, 353-358.
[3] Kenneth Levenberg (1944). “A Method for the Solution of Certain Non-Linear Problems in Least Squares,” The Quarterly of Applied Mathematics 2, 164-168.
[4] Donald Marquardt (1963). “An Algorithm for Least-Squares Estimation of Nonlinear Parameters,” SIAM Journal on Applied Mathematics 11, 431-441, doi:10.1137/0111030a.
[5] D. Lowe (1999), “Object recognition from local scale-invariant features,” Proceedings of the International Conference on Computer Vision 2: 1150-1157.